Energy Management Systems Based on Industrial Artificial Intelligence
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".
Deadline for manuscript submissions: 28 February 2025
Special Issue Editors
Interests: computational-artificial intelligence; intelligent control; evolutionary computation; neural networks; systems optimization; distributed artificial intelligence; multi-agent management systems; hybrid intelligent systems and development intelligent systems in biomedicine and energy management in buildings/hospitals
Special Issues, Collections and Topics in MDPI journals
Interests: renewable energy technology (including solar photovoltaics and microgrids for rural and remote areas); sustainable technologies for agriculture; energy efficiency
Special Issue Information
Dear colleague,
Artificial intelligence (AI) has dramatically altered the dynamic landscape of science and industry in the past several years. Industrial AI (IAI), defined as “ a systematic discipline, which focuses on developing, validating, and deploying various machine learning algorithms for industrial applications with sustainable performance,” can power the sustainable performance of energy management systems (EMSs) and the critical aspect of optimizing energy distribution. IAI can actualize smart and resilient industrial EMSs that are fault-tolerant, self-organizing, and can predict potential breakdowns in critical energy infrastructure. IAI focuses on developing EMSs for the management of renewable energy in smart grids and the challenge of managing the additional load on grids due to electric vehicles (EVs).
This Special Issue aims to present recent and high-quality research on the development of AI techniques, such as optimized solutions, industrial data modeling, and control, for improving industrial EMSs and building EMSs (BEMSs).
Topics of interest for publication include, but are not limited to, the following:
- IAI in demand-side management (DSM)
- IAI in building energy management system (BEMS)
- IAI and IoT energy management systems
- Forecast energy demand by AI
- Identify inefficiencies in EMS by AI
- Energy optimization by AI
- IAI in renewable energy systems
- IAI in energy hubs and microgrids
- IAI in smart grids
- IAI in smart cities
- IAI for sustainable EMS
- Energy trading with AI
- Prediction of renewable energy by AI
- Estimation of occupancy in buildings by AI
- IAI in electric vehicles
- Fuzzy logic energy management system
- Decision methods in EMS with AI
Prof. Dr. Anastasios Dounis
Prof. Dr. George Papadakis
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- Energy management system
- Industrial AI
- Ambient intelligence
- Distributed AI systems
- Embedded AI
- Multi-agent systems
- Intelligent control
- Decision-making
- Machine learning
- Deep learning
- Field-Programmable Gate Arrays (FPGAs) in AI systems
- Fuzzy systems
